The Human Visual System (HVS) has the ability to fixate quickly on the most informative (salient) regions of a scene and reducing therefore the inherent visual uncertainty. Computational visual attention (VA) schemes have been proposed to account for this important characteristic of the HVS. In this paper a video analysis framework based on a spatiotemporal VA model is presented. We propose a novel scheme for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. Towards this goal we extend a common image-oriented computational model of saliency-based visual attention to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step in order to obtain a compact representation of the visual content in the form of salient events/objects. The model has been implemented and qualitative as well as quantitative examples illustrating its performance are shown.